Workload Rightsizing
Analyze CPU and memory usage patterns to recommend optimal resource requests and limits. Eliminate over-provisioning without risking performance.
How it Works
Three steps from setup to savings
Collect
The agent continuously profiles CPU and memory usage per container over a configurable 14-day window. No code changes needed.
Analyze
Statistical analysis (P50-P99) with growth and spike buffers generates safe recommendations per workload.
Review
Review recommendations in the dashboard and export optimized YAML manifests for your team to apply.
What's Included
Intelligent Resource Optimization
Container-Level Analysis
Per-container CPU and memory profiling with p50, p95, and p99 usage metrics over configurable time windows.
- Compare provisioned, requested, and actual usage patterns for CPU and memory
- Proactively identify over-provisioned containers to reduce cloud waste
Smart Recommendations
Statistical recommendations that account for traffic patterns, cron jobs, and seasonal variations.
- Algorithm accounts for peak usage, daily cycles, and weekly traffic patterns
- Confidence scores help you prioritize which recommendations to apply first
Safety Guardrails
Configurable safety margins ensure recommendations never risk OOMKills or CPU throttling.
- Set custom safety margins per namespace or workload criticality level
- Automatic detection of bursty workloads that need headroom above p99
Before/After Tracking
Track actual savings after applying recommendations with side-by-side utilization comparisons.
- Side-by-side comparison of resource usage before and after optimization
- Cumulative savings tracker showing realized cost reductions over time
CI/CD Integration
Integrate rightsizing checks into your deployment pipeline to catch over-provisioning before it ships.
- GitHub Actions and GitLab CI integrations for automated resource checks
- Block deployments that exceed resource thresholds in your pipeline
Frequently Asked Questions
Common questions about Workload Rightsizing
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